Car accidents remained to be the primary cause of deatb 's worldwide. About 1.5 billion people die in road accidents per year, the vast majority of which are due to a single factor, the driver's drowsiness. Most people drive long distances without sleeping and by using mobile phones while driving, which causes tiredness which results in driver drowsiness. This can be prevented by triggering an alarm that can make the driver active when the driver becomes drowsy. Machine learning and computer vision can assist in determining if a person is drowsy or not by tracking facial landmarks that include eye and mouth patterns. A system that warns the driver if he or she becomes drowsy can make the user active. Both the eye movements and the mouth movements associated with yawning are considered, which allows in identifying if the individual is sleepy or not, in a more precise manner using Convolutional neural networks and OpenCV. This model developed will assist everyone in mitigating injuries caused by drowsiness.


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    Title :

    An Enhanced Driver Drowsiness Detection System using Transfer Learning


    Contributors:
    Sowmya Laxshmi, M.V. (author) / U, Prabu. (author) / Chandana, L. (author) / N, Sunny. (author)


    Publication date :

    2021-12-02


    Size :

    1103194 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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